AMD Instinct MI350X vs NVIDIA Rubin GPU Comparison

The Verdict

The database records two distinct accelerators aimed at different segments of the AI compute market. The AMD Instinct MI350X, based on CDNA 4.0, targets high-throughput inference and training with a focus on memory capacity and power efficiency per module. The NVIDIA Rubin GPU, built on the Rubin architecture, delivers significantly higher raw compute throughput and memory bandwidth, positioning it for peak-performance leadership in the largest-scale deployments. The data shows no direct benchmark wins for either part, as both hold a 50th percentile ranking among all GPUs with no recorded average benchmark scores. The MI350X suits environments prioritizing a 1000 W power envelope and a PCIe 5.0 interface, while the Rubin GPU commands a 2300 W TDP and PCIe 6.0 connectivity, indicating a design for systems with substantial power delivery and cooling infrastructure. Neither part offers display outputs, confirming their server-only orientation.

Architecture Differences

The two accelerators diverge fundamentally in their underlying chip designs. The MI350X uses the MI350 256CU chip with CDNA 4.0 architecture, built on a 3 nm process at TSMC with 185,000 million transistors on a 2380 mm² die. The Rubin GPU uses the GR100 chip with the Rubin architecture, also on a 3 nm TSMC process, but packs 336,000 million transistors into a smaller 1456 mm² die. This yields a transistor density of 230.8M per mm² for the Rubin GPU versus 77.7M per mm² for the MI350X, indicating a much denser packing of logic in the NVIDIA design.

The MI350X implements 16,384 shading units with 1,024 texture mapping units and no ROPs, resulting in a 0 MPixel/s pixel rate. The Rubin GPU carries 28,672 shading units, 896 TMUs, and 24 ROPs, delivering a 54.41 GPixel/s pixel rate. Texture throughput favors the MI350X at 2,252.8 GTexel/s versus 2,031.2 GTexel/s for the Rubin GPU. The MI350X reports no tensor cores, while the Rubin GPU includes 896 tensor cores. Floating-point performance shows a clear split: the MI350X delivers 72.09 TFLOPS for both FP32 and FP16 (1:1 ratio), whereas the Rubin GPU outputs 130.0 TFLOPS FP32 and 260.0 TFLOPS FP16 (2:1 ratio), more than doubling the MI350X in FP16 compute.

Memory subsystems differ substantially. The MI350X pairs 288 GB of HBM3e with an 8192-bit bus, yielding 8.19 TB/s bandwidth. The Rubin GPU matches the 288 GB capacity but uses HBM4 on a 16384-bit bus, more than doubling bandwidth to 22.1 TB/s. Clock behavior also diverges: the MI350X runs a 1000 MHz base and 2200 MHz boost, with memory at 2000 MHz (8 Gbps effective). The Rubin GPU has a lower 700 MHz base but a higher 2267 MHz boost, with memory at 2695 MHz (10.8 Gbps effective).

Form factors and system integration differ as well. The MI350X ships as an OAM Module with dimensions of 102 mm length and 165 mm width, no power connectors listed, and a suggested PSU of 1400 W. The Rubin GPU comes as an SXM Module with no recorded dimensions, no power connector details, and a suggested PSU of 2700 W. The MI350X uses PCIe 5.0 x16, while the Rubin GPU moves to PCIe 6.0 x16. Both parts have no display outputs and list N/A for DirectX, OpenGL, and Vulkan APIs. Release timing places the MI350X on 2025-06-11 and the Rubin GPU on 2025-12-31. The MI350X's predecessor is Radeon Instinct, while the Rubin GPU's predecessor is Server Blackwell. The Rubin GPU is marked as Active production status; the MI350X has no production status recorded.

FAQ

Q: Which accelerator provides higher FP16 compute throughput?

A: The NVIDIA Rubin GPU delivers 260.0 TFLOPS FP16 (2:1), which is 3.6 times the MI350X's 72.09 TFLOPS FP16 (1:1).

Q: How do the memory bandwidth figures compare between the two parts?

A: The Rubin GPU reaches 22.1 TB/s using HBM4 on a 16384-bit bus, while the MI350X achieves 8.19 TB/s with HBM3e on an 8192-bit bus. The Rubin GPU's bandwidth is 2.7 times higher.

Q: What are the power requirements for each accelerator?

A: The MI350X has a TDP of 1000 W with a suggested PSU of 1400 W. The Rubin GPU has a TDP of 2300 W and a suggested PSU of 2700 W.

Q: Do both accelerators use the same process node?

A: Yes, both are fabricated on a 3 nm process at TSMC, but the Rubin GPU packs more transistors (336,000 million) into a smaller die (1456 mm²) compared to the MI350X (185,000 million transistors on 2380 mm²).

Q: Which accelerator has a higher texture fill rate?

A: The MI350X records a texture rate of 2,252.8 GTexel/s, slightly ahead of the Rubin GPU's 2,031.2 GTexel/s, despite the Rubin GPU having more shading units.

Q: What are the release dates for these products?

A: The MI350X was released on 2025-06-11, while the Rubin GPU followed later on 2025-12-31.

Specification Differences

| Field | AMD Instinct MI350X | NVIDIA Rubin GPU |

|-------|---------------------|------------------|

| Chip | MI350 256CU | GR100 |

| Architecture | CDNA 4.0 | Rubin |

| Generation | Instinct (MIx) | Server Rubin (Rxx) |

| Transistors | 185,000 million | 336,000 million |

| Die Size | 2380 mm² | 1456 mm² |

| Transistor Density | 77.7M / mm² | 230.8M / mm² |

| Base Clock | 1000 MHz | 700 MHz |

| Boost Clock | 2200 MHz | 2267 MHz |

| Memory Clock | 2000 MHz 8 Gbps effective | 2695 MHz 10.8 Gbps effective |

| Memory Type | HBM3e | HBM4 |

| Bus Width | 8192 bit | 16384 bit |

| Memory Bandwidth | 8.19 TB/s | 22.1 TB/s |

| Shading Units | 16384 | 28672 |

| TMUs | 1024 | 896 |

| ROPs | 0 | 24 |

| Tensor Cores | null | 896 |

| Pixel Rate | 0 MPixel/s | 54.41 GPixel/s |

| Texture Rate | 2,252.8 GTexel/s | 2,031.2 GTexel/s |

| FP32 | 72.09 TFLOPS | 130.0 TFLOPS |

| FP16 | 72.09 TFLOPS (1:1) | 260.0 TFLOPS (2:1) |

| TDP | 1000 W | 2300 W |

| Slot Width | OAM Module | SXM Module |

| Suggested PSU | 1400 W | 2700 W |

| Bus Interface | PCIe 5.0 x16 | PCIe 6.0 x16 |

| Dimensions | 102 mm x 165 mm | Not recorded |

| Release Date | 2025-06-11 | 2025-12-31 |

| Predecessor | Radeon Instinct | Server Blackwell |

| Production Status | Not recorded | Active |

Head-to-Head Benchmarks

The recorded data contains no direct head-to-head benchmark results, and both accelerators share identical percentile rankings at 50th among all GPUs with zero average benchmark scores. However, the specification data enables meaningful performance comparisons across several compute metrics.

The largest margin of victory for the Rubin GPU appears in FP16 throughput. The Rubin GPU's 260.0 TFLOPS represents a 261% advantage over the MI350X's 72.09 TFLOPS. This 3.6x gap suggests that workloads relying heavily on half-precision arithmetic, common in AI training and inference, would see substantially higher throughput on the Rubin GPU. The FP32 comparison also favors the Rubin GPU, with 130.0 TFLOPS versus 72.09 TFLOPS, an 80% lead. This indicates the Rubin GPU also dominates single-precision compute, useful for scientific simulations and certain HPC workloads.

Memory bandwidth presents another decisive win for the Rubin GPU. At 22.1 TB/s, it exceeds the MI350X's 8.19 TB/s by 170%. This 2.7x bandwidth advantage, combined with the wider 16384-bit bus versus 8192-bit, suggests the Rubin GPU can feed its larger compute throughput more effectively. For memory-bound workloads such as large language model inference with massive batch sizes, the bandwidth differential could be the primary performance differentiator.

The MI350X scores its most significant win in texture rate, posting 2,252.8 GTexel/s against the Rubin GPU's 2,031.2 GTexel/s, a 10.9% lead. This advantage likely stems from the MI350X's higher boost clock relative to its core count configuration. The MI350X also holds a much lower TDP at 1000 W versus 2300 W, meaning it delivers its texture throughput at less than half the power draw. While this is not a direct performance metric, it indicates a substantially better performance-per-watt profile for the MI350X.

Clock speeds show a mixed picture. The MI350X has a higher base clock at 1000 MHz versus 700 MHz, a 42.9% advantage that helps sustained throughput under load. The Rubin GPU counters with a higher boost clock at 2267 MHz versus 2200 MHz, a 3% edge that reflects its ability to reach higher peak frequencies. Memory clocks also favor the Rubin GPU at 2695 MHz versus 2000 MHz, a 34.8% difference that contributes to its bandwidth superiority.

The shading unit count heavily favors the Rubin GPU, which packs 28,672 units versus 16,384 on the MI350X, a 75% increase. However, the MI350X uses its 1,024 TMUs more efficiently, achieving a higher texture rate despite having 14.3% more TMUs than the Rubin GPU's 896. The Rubin GPU's 24 ROPs enable a 54.41 GPixel/s pixel rate, while the MI350X has no ROP capability at all, rendering it unsuitable for any rasterization workload.

Transistor density reveals the Rubin GPU's architectural efficiency: 230.8M transistors per mm² versus 77.7M for the MI350X, a 197% density advantage. This explains how the Rubin GPU fits 336,000 million transistors into a die that is 38.8% smaller than the MI350X's 2380 mm² package. The MI350X's larger die with fewer transistors suggests a more conservative layout, possibly prioritizing thermal management or manufacturing yield over density.

In summary, the Rubin GPU dominates compute-heavy and bandwidth-intensive workloads, with its FP16 and FP32 performance leading by 261% and 80% respectively, and memory bandwidth leading by 170%. The MI350X holds a narrower lead in texture rate at 10.9% and offers a significantly lower power envelope. Both parts share the same 288 GB memory capacity, but the Rubin GPU's HBM4 implementation extracts far more performance from that capacity. The absence of benchmark data means these figures represent theoretical maximums rather than measured application performance, but the specification deltas are substantial enough to define distinct positioning: the MI350X for power-conscious AI inference, the Rubin GPU for maximum-throughput training clusters.